Evaluation by employees of employee management on large US dairy farms
Bibliographic record
Abstract
Employees, many of whom are not native English speakers, perform the majority of work on large US dairy farms. Although management of employees is a critical role of dairy owners and managers, factors that improve employee engagement and retention are not well known. Objectives were to (1) identify key dairy farm employee management issues based on employee perceptions, (2) evaluate strengths and weaknesses of farms based on employee responses, (3) investigate differences between Latino and English-speaking employees, and (4) investigate differences in perception between employers and employees. Employees from 12 US dairy farms (each with a minimum of 10 employees) were interviewed by phone following a questionnaire provided. Employees provided their responses to 21 Likert scale questions and 8 open-ended questions. There was a wide range in employee turnover among farms (<10 to >100%). Latino employees had much shorter tenure and were more often employed in milking and livestock care than English-speaking employees. Employee perceptions differed among farms regarding whether they would recommend their farm as a place to work, teamwork within the dairy, whether rules were fairly applied, availability of tools and equipment, clear lines of supervision, and recognition for good work in the previous 15 d. Latino employees (n = 91) were more positive in many of these measures than their English-speaking counterparts (n = 77) but less often provided ideas to their employer on how to improve the business. Employers, surveyed on how they thought their employees would answer, underestimated employee responses on several questions, particularly the interest of employees in learning about dairy. When asked to cite 3 goals of the operation, there were differences among owners, managers, and employees. Although employees rated their commitment to the farm and their interest in learning as high, based on turnover, there was an obvious disparity between reality and ideal employee management. Consequently, employers should act on identified management shortfalls to improve employee retention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".